{"kind":"task","effective_mode":"full","benchmark":{"kind":"benchmark","effective_mode":"full","slug":"ds-1000","formal_name":"DS-1000","introduction":"DS-1000 builds 1,000 data-science problems from real StackOverflow questions across seven libraries including NumPy, Pandas and Matplotlib. The problems are perturbed so that recalling the original answer does not solve them.","introduction_ja":"","introduction_en":"","category":"Category not supplied","task_count":null,"acquisition_status":"Acquisition status not supplied","official_url":"https://ds1000-code-gen.github.io/","indexing_mode":"index","profile":{"resources":[],"task_format":"","scoring":"","metric":"","size":"","answer_access":"","license":"","citation":"","maintainer":"","released":"","why_hard":"","related":[],"history":"","construction":"","limitations":"","saturation":"","scores":[]}},"task_id":"34251809-6481-52a7-aa81-f8c554235c70","task_key":"default--test--822","task_revision_id":"1","upstream_id":"822","short_description":"I'd like to do some operations to my df. And there is an example below.","config":"default","split":"test","body":"{\"library\":\"Sklearn\",\"prompt\":\"Problem:\\n\\nI'd like to do some operations to my df. And there is an example below.\\ndf\\n\\nCol1   Col2         Col3\\n C      33     [Apple, Orange, Banana]\\n A      2.5    [Apple, Grape]\\n B      42     [Banana]\\nafter the operations, the df is converted into\\n\\ndf\\n\\nCol1   Col2   Apple   Orange   Banana   Grape\\n C      33     1        1        1       0\\n A      2.5    1        0        0       1\\n B      42     0        0        1       0\\nGenerally, I want this pandas column which consisting of a list of String names broken down into as many columns as the unique names.\\nMaybe it's like one-hot-encode them (note that value 1 representing a given name existing in a row and then 0 is absence).\\nCould any one give me any suggestion of pandas or sklearn methods? thanks!\\n\\nA:\\n\\n<code>\\nimport pandas as pd\\nimport numpy as np\\nimport sklearn\\ndf = load_data()\\n</code>\\ndf_out = ... # put solution in this variable\\nBEGIN SOLUTION\\n<code>\\n\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://ds1000-code-gen.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}